feat: log regression metrics as parameters in Training class

- Added a method to persist computed regression metrics (MSE, MAE, R²) as MLflow parameters during model training, enhancing model evaluation and tracking.
- Updated the Training class to log the equation path if available, improving artifact management.

model_manager/utils/models/train_model_params.py

@@ -23,6 +23,7 @@ def validate_frontend_date_format(fmt: str | None) -> None:
         allowed = ', '.join(sorted(ALLOWED_FRONTEND_DATE_FORMATS))
         raise ValueError(f'Invalid date_format "{fmt}". Allowed formats: {allowed}')
 
+
 # Model name constants
 MODEL_LINEAR_REGRESSION = 'Linear Regression'
 MODEL_POLYNOMIAL_REGRESSION = 'Polynomial Regression'